March 2026 Summaries

Zillow, a leading real estate platform in the U.S., boasts a vast dataset with 228 million monthly active users and over 130 million U.S. homes, though extracting data from it is challenging due to its dual-layer anti-bot protection. The article evaluates various tools for scraping Zillow data, highlighting Bright Data for its outstanding 98.44% success rate in independent benchmarks and features like a pre-built Zillow scraper and extensive residential IP network. Other tools such as Apify, Oxylabs, and ScrapingBee are praised for specific strengths like no-code workflows, enterprise-grade reliability, and quick setup, respectively. The text delves into the technical challenges of scraping Zillow, such as its JavaScript-heavy Next.js architecture and aggressive IP blocking, and emphasizes the need for residential proxies and advanced CAPTCHA solvers. It discusses various use cases for Zillow data, from real estate investment to mortgage lead generation, and notes that while Bright Data stands out for reliability and comprehensive features, the choice of tool depends on factors like data volume, technical resources, and budget.

Ruflo, previously known as Claude Flow, is an AI orchestration framework designed to enhance the capabilities of Claude Code and OpenAI Codex by coordinating around 100 specialized AI agents for complex software tasks. It uses swarm-based collaboration, self-learning workflows, and adaptive routing to optimize performance and enable multi-agent orchestration. Despite its strengths, Ruflo faces challenges in web data retrieval due to anti-bot measures on websites. Bright Data addresses these limitations by providing an enterprise-grade infrastructure for web search, data extraction, and interaction through tools like SERP API, Web Unlocker API, and Browser API. By integrating Bright Data and Ruflo into Claude Code setups, developers can leverage over 300 MCP tools or more than 125 skills, enabling them to perform tasks like retrieving live search results, discovering tutorials, and scraping data without encountering blocks. This integration supports scalable, AI-ready web data solutions, enhancing the agentic coding assistant's effectiveness and efficiency.

OpenClaw, a highly popular AI assistant platform, faces inherent limitations due to its reliance on large language models (LLMs), which are constrained by static data and lack of web interaction. To address these issues, the OpenClaw Bright Data plugin has been introduced, allowing the AI assistant to access real-time web data and interact with external services. This integration empowers OpenClaw with capabilities such as real-time web searches via SERP APIs, bot-bypass scraping, and full browser automation, all facilitated by Bright Data's extensive proxy network. The plugin, available on ClawHub, simplifies the process by eliminating manual API wiring and proxy setup, ensuring reliable and scalable data retrieval from popular websites. This setup enables the AI assistant to provide contextual, up-to-date information, demonstrating a significant enhancement over traditional LLM capabilities.

Apache Spark Structured Streaming is a robust stream processing engine that operates on top of the Spark SQL engine, processing live data streams as continuously appended tables. Unlike its predecessor, Spark Streaming, which uses DStreams and RDDs, Structured Streaming employs DataFrame and Dataset APIs, facilitating event-time windowing, fault tolerance through checkpointing, and seamless integration of streaming and static data. The article highlights the integration of Bright Data’s SERP API with Spark Structured Streaming to build a PySpark pipeline capable of ingesting live web search data. This integration leverages Bright Data's infrastructure to fetch search engine results pages (SERPs) without the need for managing scraping complexities such as proxies or CAPTCHAs. The pipeline uses Spark's micro-batch model to periodically retrieve and transform SERP data, enabling use cases like keyword rank tracking, news aggregation, and competitive monitoring. The tutorial demonstrates building a continuous ingestion pipeline, emphasizing fault tolerance and scalability, and suggests deploying it on platforms like Databricks for production-grade applications.

Cartesia is a developer-first platform designed for building real-time AI voice agents, featuring low-latency speech models and a comprehensive agent development stack to streamline the transition from concept to a production-ready voice agent. It incorporates two in-house models: Sonic, a text-to-speech model offering expressive speech across multiple languages, and Ink, a speech-to-text model capable of handling various accents and noise. However, voice agents need real-time data access to overcome the limitations of static knowledge within Large Language Models (LLMs), which is where integrations like Bright Data come into play. By connecting Cartesia agents to Bright Data, developers can enhance these agents with web search and data extraction capabilities using tools like the SERP API and Web Unlocker API, allowing for more accurate, context-aware, and actionable responses. This integration supports dynamic, real-time information retrieval, vital for maintaining the relevance and trustworthiness of AI voice agents. The step-by-step guide provided demonstrates how to set up a Cartesia project, integrate Bright Data services, and develop an AI voice agent capable of delivering news-style reports and engaging in interactive conversations.

Brave Search API and Bright Data serve distinct roles in the realm of data access and search solutions. Brave Search API offers a privacy-focused, independent search index optimized for AI applications, providing a 30 billion-page index with features like low-latency AI grounding and Zero Data Retention (ZDR). It is ideal for applications requiring fast AI grounding and compliance with strict data retention policies but is limited in accessing protected web pages and concurrent request handling. In contrast, Bright Data excels in live web data collection, offering real-time search engine results from major platforms like Google and Yandex, without concurrent request limits, and can access protected pages using its Web Unlocker feature. Bright Data also provides extensive historical data through its Web Archive API, making it suitable for applications needing real-world search results, multi-engine coverage, and cost efficiency at scale. Ultimately, Brave is more suited for privacy-centric AI use cases, while Bright Data is tailored for comprehensive data scraping needs at enterprise scale.

Apache Airflow and Apache Spark are powerful tools that can be integrated with Bright Data’s Web Unlocker API to create an automated lead generation pipeline. Apache Airflow acts as the orchestrator, managing the scheduling, dependencies, and execution of tasks within a data pipeline, while Apache Spark handles the large-scale data processing required for transforming and analyzing massive datasets. Bright Data’s Web Unlocker API allows for the collection of structured business data across different regions without the complexities of managing proxies or bypassing anti-bot systems. Together, these tools enable the building of a robust, scalable data pipeline that can fetch business listings, clean and deduplicate the data using Spark, and store the results for further use. The pipeline can be customized and expanded to include additional steps, such as data quality checks or integration with CRM systems, providing full control over the data collection and processing workflow. Overall, this integration provides a reliable and efficient way to collect and process business data for lead generation and other data-driven applications.

In the competitive landscape of retail and e-commerce, brands must leverage web data to gain a competitive edge, as mere real-time data is no longer sufficient. The evolving market dynamics, driven by the rise of AI and the transition from traditional retailers to marketplaces, necessitate a more sophisticated approach to data intelligence. Businesses now require a deeper, automated, and faster intelligence that goes beyond fresh web data to include trends, benchmarks, and insights into consumer behavior and market trends. Bright Data highlights the four maturity levels of web data intelligence, ranging from sporadic spot checks to AI-ready intelligence, enabling organizations to optimize decision-making processes. This progression allows for faster business decisions regarding pricing, promotions, and channel optimization, ultimately transforming web data into a strategic asset. As organizations evolve their competitive intelligence operations, they must consider global data coverage, enhanced intelligence cadence, and seamless data integration into systems like Snowflake and Databricks to maintain a strategic advantage through timely and informed decision-making.

The article explores the use of PySpark and Bright Data for executing large-scale web scraping tasks, which become inefficient when attempted with single-machine scripts due to the sheer volume of data. It emphasizes treating large URL lists as distributed datasets to effectively manage and distribute the workload across clusters, ensuring reliability as the request volume increases. By employing PySpark, the article suggests leveraging its partitioning capabilities to optimize web scraping by grouping URLs into batches, allowing for parallel processing and enhanced fault tolerance. The text further outlines patterns for running requests at the partition level, designing resilient workers that can handle retries and failures, and routing requests through a proxy network to manage traffic and avoid server blockages. It also highlights the importance of monitoring jobs, managing proxy configurations, and troubleshooting common issues to maintain performance at scale, underscoring the role of Bright Data in simplifying network and infrastructure demands.

The tutorial presents a comprehensive pipeline for building a retrieval-augmented generation (RAG) application using live web data. It integrates Bright Data for finding and scraping articles, Weaviate for storing and searching them, and Cohere for embedding and generating responses. Users can transform any topic into a searchable knowledge base by following steps that include data collection through Bright Data's SERP API and Web Unlocker, processing and chunking the data into manageable pieces, storing it in Weaviate with automatic vectorization, and querying it to generate cited answers. The process is designed to overcome challenges such as anti-bot protections and the need for fresh data, offering a complete solution from setup to querying with minimal manual intervention. The pipeline is scalable, compliant with data privacy standards, and can be adapted for various use cases, making it ideal for competitive intelligence, market research, and technical investigations. It provides detailed instructions for setup and execution, including the use of API keys and dependencies, and encourages further development for production environments with options for multi-tenancy and cost optimization.

AutoGPT is an open-source platform for creating autonomous AI agents, distinguished by its low-code, block-based interface, continuous execution, and the integration of tools, APIs, and data sources for automating complex tasks. While AutoGPT agents are robust, they face limitations inherent to language models, such as outdated information and lack of real-world interaction capabilities. To overcome these challenges, integrating Bright Data into AutoGPT allows agents to access live web content and execute sophisticated workflows. Bright Data's vast proxy network and APIs, such as the Web Unlocker API, enhance AutoGPT by enabling web scraping, search engine result collection, and structured data extraction. This integration empowers AI agents to autonomously retrieve and process live information, thereby extending their capabilities beyond those of standard language models. The blog post illustrates this integration through a step-by-step guide to building an AI agent that evaluates online articles for bookmarking potential, showcasing the synergy between AutoGPT and Bright Data.

Convex is an open-source, reactive backend platform designed to synchronize web and mobile applications by combining a database, serverless functions, authentication, and client libraries into one system. It operates as a live, reactive system, tracking and updating queries in real time as data changes, making it ideal for dynamic applications. Convex integrates well with frameworks like React, Next.js, and others, and is popular among developers for its flexibility and ease of use. When paired with Bright Data, a provider of real-time web data, Convex can efficiently handle large-scale data collection and synchronization, creating reactive applications that update seamlessly with live web data. Bright Data's infrastructure supports reliable data retrieval through web scraping, overcoming challenges like dynamic content, anti-bot measures, and scalability issues. Together, Convex and Bright Data offer a scalable and maintainable solution for building applications that require real-time data updates, as demonstrated in a Next.js-based AI Market Research Terminal application that processes and presents live web data in a user-friendly format.

Social listening is a strategic process that involves monitoring and analyzing digital conversations to gain insights into public sentiment, trends, and the perception of a brand or product. This process, which extends beyond mere mention tracking, aims to inform marketing and product decisions and enhance customer support by understanding public discourse. The blog post advocates for the use of agentic AI workflows in social listening, as they provide the ability to autonomously adapt to evolving conversations across various social media platforms, unlike traditional static pipelines. Challenges such as reliable data collection and the dynamic nature of social media platforms are addressed by agent-ready scraping tools like Bright Data’s Social Media Scraper, which offers scalable solutions for collecting and analyzing data from multiple platforms while handling anti-bot measures and data fragmentation. By integrating these tools, businesses can build efficient agentic workflows that enable deep sentiment analysis, autonomous research, and cross-platform integration, thereby transforming passive data streams into active intelligence engines that evolve with the conversation landscape.

Web data collection for Large Language Models (LLMs) is a multifaceted challenge with no one-size-fits-all solution, as the appropriate tool varies significantly based on the specific use case. Key variables include the need for structured data versus raw HTML, data freshness requirements, the method of web interaction, and the desired output format. Different tools excel in different tasks, such as SERP APIs for real-time grounding in current information, MCPs for agentic web browsing, LLM scrapers for extracting structured data from AI models themselves, e-commerce scrapers for domain-specific data, video scrapers for multimodal training data, and web unlockers for overcoming anti-bot protections. Benchmarks from AIMultiple highlight the performance of various providers, with Bright Data often leading in critical areas such as field depth, scalability, and unique features like the x-unblock-expect for ensuring page completeness. Understanding these distinctions helps organizations select the most effective tools for their LLM data strategies, ensuring robustness and reliability in production environments.

The GEO/AEO Tracker is a free, open-source AI visibility dashboard that tracks brand mentions across six AI models: ChatGPT, Perplexity, Gemini, Grok, Google AI Mode, and Microsoft Copilot, using Bright Data's LLM Scrapers. This tool allows companies to monitor their brand's presence in AI-generated answers, which are becoming increasingly influential in consumer decision-making, as evidenced by McKinsey's projection of $750 billion in US revenue flowing through AI-powered search by 2028. The Tracker's features include a Prompt Hub for running simultaneous prompts across all models, Visibility Analytics for scoring brand mentions, and Citation Opportunities for identifying content gaps. It operates on a local-first architecture, ensuring data sovereignty and avoiding vendor lock-in. The tool was developed to address the limitations and high costs of existing solutions, using Bright Data's infrastructure to simplify the complex process of scraping AI models, which are often aggressively bot-blocked and structurally different. The Tracker supports various enterprise use cases, such as brand reputation monitoring, competitive intelligence, and technical GEO audits, making it a scalable solution for businesses seeking to optimize their AI visibility.

Exa is a semantic search engine designed for AI applications, utilizing a custom neural index for conceptual relevance, while Bright Data provides web data infrastructure, offering real-time access to live web content, including pages protected by anti-bot measures. The two serve fundamentally different purposes: Exa excels in semantic searches and fast developer onboarding, with unique features like the "Find Similar" functionality and specialized indexes for people and companies. Conversely, Bright Data is tailored for large-scale operations requiring real-time, accurate data from multiple search engines and protected web pages, offering historical data through its extensive web archive. Bright Data is more cost-effective at scale and offers unlimited concurrent requests, making it suitable for production environments, whereas Exa is more applicable for early-stage research or specific use cases like semantic similarity searches. Both tools can be complementary, with Exa used for initial semantic discovery and Bright Data for verification and detailed data extraction at scale.

Tableau, a premier data visualization tool, cannot natively scrape live data from websites, especially since the deprecation of its Web Data Connector (WDC v2) in 2023 has left analysts without a supported solution for direct data extraction. The text explores six alternative methods for integrating live web data into Tableau, emphasizing the need for an external data pipeline to handle dynamic web data requirements, such as competitor pricing and social media metrics. The most effective method, as recommended, is utilizing Bright Data's Web Scraper API, which automates the complex data collection process, including proxy rotation, CAPTCHA solving, and JavaScript rendering, to deliver structured data in a format compatible with Tableau. This approach streamlines the process for users, allowing them to focus on dashboard creation rather than the intricacies of data collection infrastructure, although it involves a paid service. Other methods like Google Sheets, Excel, and DIY Python scripts are discussed but noted for their limitations, particularly at scale. The text provides a detailed tutorial on setting up an API-to-Tableau pipeline for competitor price monitoring, demonstrating how to use the Bright Data API to fetch and visualize data efficiently in Tableau.

The integration of Nanobot, a personal AI agent framework, with the Bright Data MCP Server enables autonomous web monitoring and data extraction by providing access to 65 specialized web tools for search, scraping, and browser automation. This setup allows the AI agent to autonomously monitor websites, remember changes, and report back, while Bright Data handles complexities like IP blocks and JavaScript rendering. Users can set up this system by configuring a simple JSON file in about 15 minutes without custom code, and the free tier of Bright Data allows 5,000 requests per month. Nanobot, developed by the HKUDS Lab at the University of Hong Kong, supports various external tools through MCP and integrates with multiple communication platforms such as Telegram, Discord, WhatsApp, and Slack. The Bright Data MCP Server offers specialized data extraction tools for various domains, including eCommerce, social media, and business intelligence, with structured data extractors available in the Pro tier, which requires a subscription beyond the free tier. This tutorial provides step-by-step guidance on setting up the integration, running demos, and troubleshooting, aiming to enhance users' ability to automate data gathering and monitoring tasks efficiently.

The guide details the process of integrating Bright Data Web MCP with Google's Vertex AI Agent Builder to create an AI market research agent capable of live web access, all within the Google Cloud Console UI without the need for coding. It explains how to use the Vertex AI Agent Builder, a low-code platform, to manage state, tool orchestration, and governance, allowing the AI agent to access real-time data via Bright Data's tools. The integration involves creating an agent, connecting Bright Data's MCP for live web scraping, and testing the agent's capabilities in a simulator. This setup enhances the agent's ability to provide accurate, up-to-date market insights by leveraging live data, especially on JavaScript-heavy pages, through browser automation tools. The guide emphasizes the ease of setting up and testing the integration, enabling users to quickly prototype AI agents without complex infrastructure or backend coding.

AWS Step Functions is a fully managed service that facilitates the orchestration and automation of complex workflows across various AWS services, making it particularly well-suited for tasks such as web scraping. It employs state machines to manage workflows consisting of multiple steps, simplifying orchestration and monitoring while providing built-in error handling and parallel execution capabilities. The guide details how Bright Data can be integrated into AWS Step Functions to enhance web scraping efforts by overcoming challenges such as anti-bot protections. This integration can be achieved via direct API calls or through AWS Lambda functions, allowing for scalable and reliable data retrieval and processing workflows. Bright Data offers solutions like the SERP API and Web Unlocker to bypass web restrictions and automate data extraction, which can be seamlessly incorporated into AWS Step Functions to build robust, end-to-end data pipelines.

AWS Glue is a serverless data integration service designed to facilitate the discovery, preparation, and combination of data from various sources, allowing users to build ETL (Extract, Transform, Load) workflows for analytics and machine learning without managing infrastructure. It offers features such as schema inference, data cataloging, and job authoring tools, which simplify data pipeline creation and monitoring. Bright Data enhances AWS Glue ETL workflows by offering real-time, structured web data extraction, which can be used to enrich datasets, verify data accuracy, and provide insights that are not easily accessible through traditional means. The tutorial demonstrates how to integrate Bright Data into an AWS Glue ETL pipeline, showcasing the extraction of stock data from Yahoo Finance using Bright Data's web scraping APIs and the transformation of this data with SQL queries before storing it in an Amazon S3 bucket. This integration illustrates the potential of combining AWS Glue with Bright Data to build robust, scalable, and informative data pipelines.

Bright Data is recognized as the leading web scraping API in 2026, boasting a 98.44% average success rate according to Scrape.do's independent benchmark, outperforming 11 other providers. Its extensive network includes over 150 million residential IPs across 195 countries, and it offers 437 pre-built scrapers for numerous domains, including major platforms like Amazon and LinkedIn. The web scraping market, valued at $1.03 billion in 2025, is expected to grow to $2.23 billion by 2030, with Bright Data catering to this demand through its comprehensive infrastructure that includes automatic proxy rotation, JavaScript rendering, and CAPTCHA solving. The API's pricing is competitive, starting at $1.50 per 1,000 successful requests, and it is compliant with GDPR, CCPA, and ISO 27001 standards. Bright Data serves a variety of use cases, from AI training and e-commerce price monitoring to social media data collection, with its infrastructure designed to handle high-volume data pipelines efficiently.

This tutorial provides a comprehensive guide for scraping video data from Bilibili, a major Chinese video platform, to support various use cases, including AI training and market analysis. The process involves using Python and browser automation tools like Playwright to navigate Bilibili’s dynamic interface and collect video metadata such as titles, URLs, and engagement statistics. It further explains how to download videos using yt-dlp, a tool for extracting multimedia content, and discusses the challenges of maintaining a custom scraping solution due to Bilibili’s anti-bot mechanisms. To overcome these challenges, the tutorial suggests using Bright Data’s Bilibili Scraper, which offers a robust infrastructure with automatic anti-bot bypass capabilities and supports large-scale data collection for enterprises. This approach ensures reliable access to vast amounts of video data, which is crucial for training multimodal AI models.

MLflow is an open-source platform designed to manage the entire machine learning lifecycle, offering features for tracking, reproducing, and deploying models across various environments like Python, R, and Java. It supports both traditional and deep learning workflows with tools for experimentation, versioning, evaluation, and deployment in a reproducible and collaborative manner. The platform's language-agnostic nature and flexibility make it suitable for diverse setups and it boasts significant community support with over 24k stars on GitHub. The tutorial emphasizes using web-scraped datasets, such as those from Bright Data, to enhance machine learning experiments due to their diversity and scale, which capture real-world distributions and variability. The guide details setting up an MLflow experiment to build a machine learning pipeline using a Random Forest model to predict product prices based on features like ratings and reviews. It covers the steps for preparing the dataset, setting up the environment, and tracking experiments using MLflow's features, highlighting the importance of system metrics and model performance evaluation. Despite the technical success of setting up the experiment, the results indicate that the current pipeline may not adequately capture the underlying patterns due to the modest R² and high RMSE, suggesting the need for feature expansion and alternative modeling approaches.

The blog post outlines the integration of a dedicated web data collection component into Kubeflow Pipelines to enhance machine learning workflows with real-time, structured data. It emphasizes the benefits of using up-to-date web-scraped data, particularly for applications like TikTok sentiment analysis. The post guides readers through building a Kubeflow pipeline that connects to TikTok comment feeds using a scraping solution such as Bright Data, which offers reliable large-scale web scraping capabilities. It details the implementation of this pipeline, involving two main components: one for collecting TikTok comments and another for performing sentiment analysis on the collected data. The process involves setting up a Python environment, configuring Bright Data's Web Scraping APIs, and composing the pipeline using Kubeflow's structures. The tutorial concludes with instructions for compiling and testing the pipeline locally using Docker, emphasizing the importance of fresh data for AI-driven projects.

Azure Synapse Analytics is a cloud-based platform that integrates data integration, enterprise data warehousing, and big data processing into a unified workspace, allowing users to ingest, transform, and query large volumes of data. The platform is particularly effective for building data pipelines for business intelligence, as demonstrated by its integration with Bright Data’s SERP API to create a web data pipeline that collects, transforms, and analyzes search engine results. This integration facilitates the ingestion of real-time web search data into data warehouses without needing to maintain scraping infrastructure, making it ideal for applications like SEO keyword tracking, competitive intelligence, and market research. Unlike Azure AI Foundry, which focuses on AI application development and management, Azure Synapse excels in large-scale data processing and analytics, making it complementary to AI Foundry by providing a robust data foundation. The tutorial outlined in the article guides users through setting up a Synapse pipeline with a Spark pool for data transformation, highlighting the steps to configure the environment, ingest data using REST APIs, and use Apache Spark for data transformation into analytics-ready formats.

Azure Logic Apps is a cloud-based platform for creating automated workflows with minimal coding, using a visual designer and over 1,400 prebuilt connectors to integrate services, systems, apps, and data sources across various environments. It is particularly useful for automating business processes and orchestrating tasks across services like Gmail, Office 365, and Salesforce. This article highlights the benefits of integrating Bright Data’s SERP API into Azure Logic Apps to build automated web search workflows. The integration allows users to fetch real-time search results from search engines such as Google and utilize them in business workflows without the need for scraping infrastructure. The tutorial provided demonstrates how to set up a news monitoring and alerting system that runs on a schedule, retrieves news articles using Bright Data's SERP API, parses the results, and sends email alerts, all without writing code. This approach offers a reliable way to access structured, real-time web data and can be extended for various applications, such as lead generation, content curation, and price monitoring.

This comprehensive guide outlines how to build a serverless web scraping pipeline using Google Cloud services, including Cloud Run, Firestore, BigQuery, Workflows, and Cloud Scheduler. It emphasizes the benefits of a serverless architecture, such as cost efficiency and scalability, by only charging for resources when services are actively handling requests. The guide details the setup process, from creating the Google Cloud infrastructure and deploying services for scraping and data exposure, to orchestrating workflows and automating tasks with a scheduler. It explains the use of Firestore for job tracking, BigQuery for data analytics, and how to ensure the pipeline functions end-to-end. The article also discusses the importance of setting up appropriate IAM permissions and testing the services to ensure they operate as intended. Finally, it provides insights into CI/CD integration with Cloud Build and offers alternative approaches for managing web scraping tasks on different platforms.